Chatbots in 2017 -- Ithaca Talk Dec 6
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The document discusses the evolution of chatbots, highlighting their current applications like in Facebook Messenger and Alexa, alongside their historical development from early interactions with machines. It covers key technological milestones, significant figures in AI history, and the challenges of building effective conversational interfaces while also forecasting future trends in the chatbot landscape. The importance of data-rich environments and interdisciplinary approaches is emphasized for the advancement of chatbots.
Chatbots in 2017 -- Ithaca Talk Dec 6
- 1. Chatbots Present, Past and Future Paul Houle Ontology2
- 3. Facebook Messenger Amazon Echo (Alexa) Interact with chatbots using the same UI you use to interact with people Conversational voice interface oriented around tasks; voice is the primary, if not the only interface
- 4. Facebook Messenger Platform Message content: 1. Text 2. Video, Audio and Image Media Objects 3. Hyperlinks, Buttons, “Call to Action” Application Server Back-End Business Systems Real World Human Supervision
- 5. Facebook Messenger: Air Travel Application
- 6. Air Travel Application • Large Commercial Value (3 tickets, $4000) • Complex Interaction that takes place over many days • Takes place in moderately large, but finite world • Mixed-initiative, changes can occur on either end: • Passenger: change flight, seats, upgrade, ... • Airline: cancellation, delay, standby, ...
- 7. Your IT Systems Alexa Skills: System architecture
- 9. The trouble with mobile applications Complex Business Complex Applications Client Communication Networks App Stores A complex business means a complex application: hard to distribute as a mobile app if all screens are sent on the client a real business application can have 200-2000 screens!
- 10. Asynchronous/Mixed Initiative User Interface
- 11. Chatbots past
- 12. From telegraph to teletype... 20 words/minute ≈ 2 character/sec TELEX 15 character/sec ≈ 110 bits/sec (bps)
- 13. 1770: The Turk Chess playing “robot” controlled by a very small chess player
- 14. 1942 Alan Turing: War Hero
- 15. Alan Turing: Computer Scientist 1952 First Computer Chess ProgramFundamentals of Computation
- 16. 1950: Alan Turing’s “Imitation Game” Interrogator tries to answer: “who is the male and who is the female?” Both players are pretending to be the other
- 17. Can a computer understand gender? Can a computer pretend to be gendered? Can it pretend to be the other gender? What social skills are necessary?
- 18. Can a computer pass as a human at all?
- 20. 1956 1957
- 21. 1957 Client-Centered Therapy Psychotherapist Carl Rogers Client-centered therapy mirrored not mirrored
- 22. 1960 SABRE Airline Booking System 1958 SAGE Air Defense System
- 23. Two response strategies are sufficient to elicit empathy: user fills in imagined motives for simulated computer therapist (is that different from experiencing fiction?) 1966 The Eliza Effect Active Listening Changing the Subject ELIZA: Maybe your plans have something to do with this. ELIZA: Don't you think computers can help people? ELIZA: I've told you that apologies are not required. ELIZA: Can You Elaborate On That?
- 25. 1970 SHRDLU > Pick up a big red block. OK. > Grasp the pyramid I DON'T UNDERSTAND WHICH PYRAMID YOU MEAN. > Find a block which is taller than the one you are holding and put it into the box. BY "IT", I ASSUME YOU MEAN THE BLOCK WHICH IS TALLER THAN THE ONE I AM HOLDING. OK. > What does the box contain? THE BLUE PYRAMID AND THE BLUE BLOCK. > What is the pyramid supported by? THE BOX. SHRDLU makes conversation about an imaginary “blocks world” with the computer and can order the computer to make changes to the block world within some constraints.
- 26. id type color picked_up above inside height width ... 1 block green 0 0 0 2.0 2.0 ... 2 block red 1 0 0 3.0 1.5 ... 3 pyramid red 0 1 0 3 0.5 ... 4 block blue 0 0 0 4.0 2.5 ... “The Blocks World”
- 27. SHRDLU capabilities “Have a conversation about a database” English-like syntax Remembers context and... ... asks questions to uncover context ability to reason and plan
- 28. Teleprinter Video Display Terminal
- 29. 1975 Microsoft Basic 1) Using BASIC is like having a conversation, and... 2) ... A conversation-like interface is easy to implement in BASIC
- 30. 1975 Microsoft Basic ... but the conversation context is encoded in the state of the program Not Flexible Can’t handle Mixed Initiative
- 31. 1975 MYCIN Medical Diagnostic Expert System
- 32. 1975 MYCIN Medical Diagnostic Expert System
- 33. 1975 MYCIN Medical Diagnostic Expert System Rules encode knowledge & procedures Facts describe the problem
- 34. Production Rules / RETE Engine Ideal for mixed initiative: • System accepts facts from both the user and the world (react to multiple inputs) • Firing rules can (i) cause actions and (ii) cause more rules to fire
- 35. 1976 Colossal Cave database describing game world has a lot in common with SHRDLU....
- 36. 1983 Infocom
- 38. “AI Winter” SHDRLU and MYCIN were not scalable! Algorithms and tools did not scale Labor Cost to Create Knowledge Base too High
- 41. 2001 Metal Gear Solid 2
- 42. 2016 Consumer VR
- 43. REPL (of many kinds) is alive and well but not as smart as SHRDLU!
- 45. 1985 GSM and SMS A PUBL1C SERVICE ANN0UNCEMENT!
- 46. IRC, ICQ, AIM, Skype...
- 47. 2007 iPhone
- 49. • 1-layer neural network can learn a separating line between two categories • The input space could have thousands or millions of dimensions (ex. an image) 1957 Perceptrons This 1969 book by Minsky and Papert demolished Perceptrons by demonstrating many things Perceptrons could not do
- 50. 1971 full-text search tfidf: term frequency/inverse document frequency Karen-Spärck Jones and Gerard Salton @ Cornell • is computing a dot product in high-dimensional space just like the Perceptron • is solving a “subjective” problem, isn’t expected to get 100% right answers • still the dominant algorithm for full-text search in 2016
- 51. 1992 US Post Office: Handwritten Digits Backpropagation makes it practical to train shallow neural networks. NIST developed training data for this project that eventually became the famous MNIST digits
- 52. predictive analytics Problem: given four measurements for an Iris flower, determine species data from 1936 Ronald Fisher and Edgar Anderson methods based on Vector Spaces: methods based on rules: linear discriminant, support vector machine, neural networks, k-nearest neighbors, etc. C4.5, random forests, inductive logic programming
- 53. data driven competitions 1992-present: yearly competitions and conference to improve accuracy of search engines and similar systems. Supported by US National Institute of Standards. 2010-present data: set of images annotated with noun concepts from Wordnet, yearly competitions for classification tasks have led to large breakthroughs in convolutional neural networks & image recognition 2010-present data: venture backed company solves data science problems for customers by holding public competitions
- 54. 2006 deep learning Two kinds of training data: 1. A large number of unlabeled examples 2. A small number of classified examples Two phases: 1. Deep Belief Network (DBN) learns statistical regularities in unlabeled data 2. Backpropagation fine tunes the network for a specific task based on labeled and/or unlabeled data information bottleneck forces network to generalize rather than memorize
- 55. present:Explosion of Neural Network Architectures
- 56. 2011 IBM Watson wins at Jeopardy Watson considers several possible answers to a question and computes a probability score for each one. Watson weighs the risk of getting a wrong answer against the risk of an opponent answering first and takes action at the optimal time. It calculates bets to ensure a win, if possible.
- 58. Watson Precision/Recall Curve Throwaway prototype based on commercial off- the-shelf (COTS) full-text search engine Point cloud is estimated performance of human jeopardy players, the goal is to get into this region Progressive improvement of: • knowledge base • question answering strategies • result merging
- 59. Watson achieves hyperprecision because it can choose whether or not to answer a question. Watson reasons about uncertainty in order to maximize a utility function; act in it’s own “self interest”
- 60. Chatbots future
- 61. Overwhelmed? Intelligent systems use complexity to cope with the complexity of the world they inhabit. Outsource subtasks to agents...
- 62. present:
- 63. also on
- 64. a company wit.ai: rapidly generalizes examples of specific patterns of text and links these to “intents”
- 65. business rules: revenge of the expert system!
- 66. Business Processing Modelling Language • Production rules engine scale 1000x larger • ... are ideal for managing processes which happen over an extended time • ... that are driven by events that happen in “the real world”
- 67. Complex Event Processing Greatly improved RETE algorithms do this efficiently! Rules can put together a story about a set of related events, by creating new events when the existing events meet some condition
- 68. constraint solving & optimization Route Optimization SAT/SMT Solver Travel PlanningBox Packing There are tools such as Drools OptaPlanner and IBM CPLEX Optimizer that marry constraint solving and optimization with rules-based systems. However, many people who work in this space code everything in C++ because they want to try the largest rate of possibilities per second
- 69. 2XL “toy of the year” -- Disney's Family Fun Magazine Speak and Spell 1978
- 70. 1980s Interactive Voice Response:
- 71. 2001 <VoiceXML> VoiceXML (from TellMe) supports text to speech in voice prompts and lets the script author write a grammar for things that the telephone caller is supposed to say. Performance is dependent on the system modelling what the user might say: it can resolve addresses in the US because it has a list of all the street names! Can call out to “Web Services” in order to implement business tasks
- 72. 2000 Voice Improvement Program Today: A chance of rain after 4pm. Increasing clouds, with a high near 41. Southeast wind 6 to 8 mph. Chance of TODAY: A CHANCE OF RAIN AFTER 4PM. INCREASING CLOUDS, WITH A HIGH NEAR 41. SOUTHEAST WIND 6 TO 8 MPH. CHANCE OF PRECIPITATION IS 50%. NEW PRECIPITATION AMOUNTS OF LESS THAN ... • Six voices: male/female and English/Spanish • Voices vary speed and pitch to create feeling of urgency • Requires full attention to listen to ITHACA WXN59 162.5 MHZ
- 73. Video games engage players with dialog that supports the story. Dialog depends heavily on writing & voice acting and is not very interactive. Text-to-speech can’t keep users engaged
- 75. SSML
- 76. All-in Amazon Alexa is a new platform where you can’t fall back to the keyboard, mouse or touch-screen. Voice function has to be good Others Bolted onto full-powered phones, computers, and game consoles, vendors don’t have to face the hard cases for voice control because fallback to traditional controllers is imminent.
- 77. seeing the world that humans live in specialized cameras and sensors let robots see the world directly in 3-d
- 78. varieties of depth camera multiocular structured light laser scanner
- 79. Kinect/A Sensor for a Sessile Robot
- 82. Inform 7 tricks and ideas Controlled English facts and rules Pre-existing Ontologies and Theories Rules Override Other Rules Parsing number words as numbers Rule Precedence Managed with “Rulebooks”
- 83. Conclusion: Chatbots • Popular today because of mobile application limitations • Possible “third platform” for applications • Use a wide range of tactics to accomplish goals • Chatbots in 2017 will depend on data-rich services • Deeply interdisciplinary, involving art as much as science